ONNX Models

ONNX (Open Neural Network Exchange) is a widely supported format that enables you to use models from TensorFlow, Keras, scikit-learn, and other frameworks with ZETIC Melange.

TensorFlow / Keras

Use tf2onnx to convert TensorFlow and Keras models to ONNX format.

Installation

pip install tf2onnx

From a SavedModel Directory

python -m tf2onnx.convert --saved-model saved_model_dir --output model.onnx --opset 13

From a Keras Model (Python API)

import tensorflow as tf
import tf2onnx

# Load your model
model = tf.keras.models.load_model("my_model.h5")

# Convert to ONNX
spec = (tf.TensorSpec((1, 224, 224, 3), tf.float32, name="input"),)
output_path = "model.onnx"

model_proto, _ = tf2onnx.convert.from_keras(model, input_signature=spec, output_path=output_path)

From a TFLite Model

python -m tf2onnx.convert --tflite model.tflite --output model.onnx --opset 13

We recommend using opset 12 or higher for the best compatibility with Melange's compiler.

Scikit-Learn

Use skl2onnx to convert scikit-learn models to ONNX format.

Installation

pip install skl2onnx

Conversion

from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import FloatTensorType

initial_type = [('float_input', FloatTensorType([None, 4]))]
onx = convert_sklearn(model, initial_types=initial_type)
with open("model.onnx", "wb") as f:
    f.write(onx.SerializeToString())

Saving Sample Inputs

After converting your model, save sample inputs as NumPy files for upload:

import numpy as np

# Create a sample input matching your model's expected shape
sample_input = np.random.randn(1, 224, 224, 3).astype(np.float32)
np.save("input.npy", sample_input)

Simplifying ONNX Models

If you encounter conversion issues, use onnx-simplifier to reduce complex subgraphs:

pip install onnxsim
onnxsim input_model.onnx output_model.onnx

Simplifying your ONNX model can resolve many compilation issues by removing redundant operations and folding constant expressions.

Other Frameworks

For other frameworks that support ONNX export, refer to the ONNX Tutorials.

Next Steps

Supported Formats: Verify input order and shapes
Web Dashboard: Upload your ONNX model
CLI: Deploy via command line

ONNX Models

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